Artificial intelligence in the healthcare industry is predicted to save $150 billion annually for the US. As such, AI is being rapidly deployed in many areas of the healthcare landscape. This event will primarily focus on the Providers, attracting CIOs,
CTOs, IT and Informatics Experts along with senior Physicians and Clinicians from the leading US hospitals who will share their experiences of using AI in the clinical care and hospital operations environment.

It is a very exciting time in healthcare. New types of data that could improve the way we care for patients are increasingly becoming available. AI techniques pioneered in other industries are opening the promise of enhanced clinical decision
support based on higher dimensional data than humans can consider. But at the same time clinical processes are resistant to change. Broadly taking advantage of advances inherently involves altering care delivery paradigms. This talk
will focus this “last mile” problem and focus on infrastructure being constructed to help address it.

AI holds tremendous promise for improving our ability to detect and potentially prevent hospital-acquired infections. This talk will focus on Clostridium difficile, which results in 500,000 infections each year and 30,000 deaths in the US, and our
team’s approach to early detection using machine learning.

2:45 MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market

Harry Glorikian, Healthcare Consultant

3:00 Refreshment Break in the Exhibit Hall with Poster Viewing

3:45 Examining the use of AI for Imaging in Clinical Care

Aalpen A. Patel, MD, Chair, Department of Radiology, Geisinger
Health

In recent years, deep learning has revolutionized the field of computer vision. In ImageNet competition, deep learning models are now outperforming humans in object detection and classification. In medical imaging, deep learning has been used in variety
of image processing tasks such as segmentation and in recent years, for diagnostic purposes such as diabetic retinopathy and skin cancer detection using large medical datasets. More recently, we have published a paper describing DL based
identification of intracranial haemorrhage on CT scans of the head and using it to prioritize the list for interpretation. We believe that using large clinical grade, heterogenous data set is extremely valuable in generalizing and translating
to clinical tools. This is just the beginning – combining all the -ologies, -omics with imaging will lead to insights we have not had before.

Interest in AI-enabled healthcare applications is growing, but there is a gap between demonstrating proof-of-concept and widespread clinical adoption. The MGH and BWH Center for Clinical Data Science is focused on the full lifecycle of research
and development of machine learning applications for healthcare, through to clinical integration and translation. Example AI applications in imaging will be discussed including their promise as well as current limitations.

We are in an exponential trajectory towards meshed thinking in carbon (our brains) and in silico (our machines). There are inherent biases that characterize either form of cognition. The science of cognitive bias in carbon is well
defined but managing those known biases are non-existent to primitive at best. The science of machine bias is very nascent at best. The notion of meshed cognition and hence meshed bias is completely terra nova, and should become
the focus of deep research by multi-disciplinary teams that span cognitive bias, machine bias, and the future of computer human interfaces as it represents the future of meshed bias and potential mitigation strategies.

We have developed a series of physician order entry-driven clinical decision support tools to facilitate and optimize ordering of laboratory tests. These include reminders for redundant tests, evidence-based guidelines for appropriate ordering
and timing of therapeutic drugs such as antiepileptic agents, digoxin, vancomycin and others. We also developed a computerized alerting system for communicating critical laboratory results to provider. We present the results from various
clinical studies we performed to evaluate the clinical effectiveness of our interventions.

With advances of machine intelligence in healthcare, key stakeholders risk suffering from an inflation of expectations and misunderstanding of capabilities. This talk will summarize key conceptual underpinnings of machine learning methods
and discuss academic and industry implementation examples of AI in healthcare. The goal of this talk is support participants in adroit critical thinking as they face potential applications, initiatives, and products involving AI in healthcare.

3:05 Refreshment Break in the Exhibit Hall - Last Chance for Viewing

3:40 PANEL: AI and Advanced Algorithms in Healthcare from the Investors Perspective

Investing in AI start-ups in the healthcare industry

Real world applications of AI in the healthcare industry

Regulatory hurdles for integrating AI in the US

Impact of AI on future jobs in the healthcare industry: Will the AI doctor see you now?

Viable business models and opportunities to unlock value through AI in healthcare for patients

How are emerging partnerships forming between integrated health systems, R&D and real-world evidence arms of Pharma?

The key for AI to work in healthcare is to have clean data to run through the algorithms

For companies that are in the AI of healthcare space, it is important to have a use case for the product versus just having a great algorithm